C / CUDA C / PyCUDAの基礎Programming Tutorial

%%writefile cpuAdd.c
#include <stdio.h>

int main(void)
{
int N = 10;
float a[N],b[N],c[N];

for (int i = 0; i < N; ++i){
	a[i] = i;
	b[i] = i*i;	
}

for (int i = 0; i < N; ++i){
	c[i]= a[i]+b[i];	
}

for (int i = 0; i < N; ++i){
	printf("%f \n",c[i]);	
}
return 0;
}

!g++ cpuAdd.c -o cpua

!./cpua

%%writefile gpuAdd.cu
#include <stdio.h>
#include <cuda_runtime.h>
// CUDA Kernel
__global__ void vectorAdd(const float *A, const float *B, float *C, int numElements)
{
    int i = blockDim.x * blockIdx.x + threadIdx.x;
    if (i < numElements)
    {
        C[i] = A[i] + B[i];
    }
}

/**
 * Host main routine
 */
int main(void)
{
    int numElements = 15;
    size_t size = numElements * sizeof(float);
    printf("[Vector addition of %d elements]\n", numElements);

    float a[numElements],b[numElements],c[numElements];
    float *a_gpu,*b_gpu,*c_gpu;

    cudaMalloc((void **)&a_gpu, size);
    cudaMalloc((void **)&b_gpu, size);
    cudaMalloc((void **)&c_gpu, size);

    for (int i=0;i<numElements;++i ){

        a[i] = i*i;
        b[i] = i;

    }
    // Copy the host input vectors A and B in host memory to the device input vectors in
    // device memory
    printf("Copy input data from the host memory to the CUDA device\n");
    cudaMemcpy(a_gpu, a, size, cudaMemcpyHostToDevice);
    cudaMemcpy(b_gpu, b, size, cudaMemcpyHostToDevice);

    // Launch the Vector Add CUDA Kernel
    int threadsPerBlock = 256;
    int blocksPerGrid =(numElements + threadsPerBlock - 1) / threadsPerBlock;
    printf("CUDA kernel launch with %d blocks of %d threads\n", blocksPerGrid, threadsPerBlock);
    vectorAdd<<<blocksPerGrid, threadsPerBlock>>>(a_gpu, b_gpu, c_gpu, numElements);

    // Copy the device result vector in device memory to the host result vector
    // in host memory.
    printf("Copy output data from the CUDA device to the host memory\n");
    cudaMemcpy(c, c_gpu, size, cudaMemcpyDeviceToHost);

    for (int i=0;i<numElements;++i ){
        printf("%f \n",c[i]);
    }

    // Free device global memory
    cudaFree(a_gpu);
    cudaFree(b_gpu);
    cudaFree(c_gpu);

    printf("Done\n");
    return 0;
}

!nvcc gpuAdd.cu -o gpu

!./gpu

from pycuda import autoinit
from pycuda import gpuarray
import numpy as np

aux = range(15)
a = np.array(aux).astype(np.float32)
b = (a*a).astype(np.float32)
c = np.zeros(len(aux)).astype(np.float32)

a_gpu = gpuarray.to_gpu(a)
b_gpu = gpuarray.to_gpu(b)
c_gpu = gpuarray.to_gpu(c)
c_gpu = a_gpu+b_gpu

a_gpu,b_gpu,c_gpu

from pycuda.elementwise import ElementwiseKernel

myCudaFunc = ElementwiseKernel(arguments = "float *a, float *b, float *c",
                               operation = "c[i] = a[i]+b[i]")
c_gpu.set(c)
c_gpu

myCudaFunc(a_gpu,b_gpu,c_gpu)
c_gpu

from pycuda.compiler import SourceModule

cudaCode = open("gpuAdd.cu","r")
myCUDACode = cudaCode.read()
myCode = SourceModule(myCUDACode)

importedKernel = myCode.get_function("vectorAdd")

nData = len(a)
nThreadsPerBlock = 256
nBlockPerGrid = 1
nGridsPerBlock = 1

c_gpu.set(c)
c_gpu

importedKernel(a_gpu.gpudata,b_gpu.gpudata,c_gpu.gpudata,block=(256,1,1))
c_gpu